Validation of a machine learning algorithm for identifying infants at risk of hypoxic ischaemic encephalopathy in a large unseen data set

IF 3.9 2区 医学 Q1 PEDIATRICS
Anne L Murray, Daragh S O’Boyle, Brian H Walsh, Deirdre M Murray
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Abstract

Objective To validate a hypoxic ischaemic encephalopathy (HIE) prediction algorithm to identify infants at risk of HIE immediately after birth using readily available clinical data. Design Secondary review of electronic health record data of term deliveries from January 2017 to December 2021. Setting A tertiary maternity hospital. Patients Infants >36 weeks’ gestation with the following clinical variables available: Apgar Score at 1 min and 5 min, postnatal pH, base deficit, and lactate values taken within 1 hour of birth Interventions Previously trained open-source logistic regression and random forest (RF) prediction algorithms were used to calculate a probability index (PI) for each infant for the occurrence of HIE. Main outcome Validation of a machine learning algorithm to identify infants at risk of HIE in the immediate postnatal period. Results 1081 had a complete data set available within 1 hour of birth: 76 (6.95%) with HIE and 1005 non-HIE. Of the 76 infants with HIE, 37 were classified as mild, 29 moderate and 10 severe. The best overall accuracy was seen with the RF model. Median (IQR) PI in the HIE group was 0.70 (0.53–0.86) vs 0.05 (0.02–0.15), (p<0.001) in the non-HIE group. The area under the receiver operating characteristics curve for prediction of HIE=0.926 (0.893–0.959, p<0.001). Using a PI cut-off to optimise sensitivity of 0.30, 936 of the 1081 (86.5%) infants were correctly classified. Conclusion In a large unseen data set an open-source algorithm could identify infants at risk of HIE in the immediate postnatal period. This may aid focused clinical examination, transfer to tertiary care (if necessary) and timely intervention. Data may be obtained from a third party and are not publicly available.
在大型未见数据集中验证识别婴儿缺氧缺血性脑病风险的机器学习算法
目的 验证缺氧缺血性脑病(HIE)预测算法,利用现成的临床数据识别出生后即面临 HIE 风险的婴儿。设计 对2017年1月至2021年12月的足月分娩电子健康记录数据进行二次回顾。地点 一家三级妇产医院。患者 妊娠期大于 36 周且具备以下临床变量的婴儿:1分钟和5分钟的Apgar评分、出生后1小时内的pH值、碱缺失值和乳酸值 干预方法 使用之前训练过的开源逻辑回归和随机森林(RF)预测算法计算每个婴儿发生HIE的概率指数(PI)。主要结果 验证了一种机器学习算法,该算法用于识别产后即刻出现 HIE 风险的婴儿。结果 1081 名婴儿在出生后 1 小时内获得了完整的数据集:76 名婴儿(6.95%)患有 HIE,1005 名婴儿未患有 HIE。在 76 名 HIE 婴儿中,37 名被归类为轻度,29 名被归类为中度,10 名被归类为重度。射频模型的总体准确率最高。HIE 组的 PI 中位数(IQR)为 0.70(0.53-0.86),非 HIE 组为 0.05(0.02-0.15),(P<0.001)。预测 HIE 的接收者操作特征曲线下面积=0.926(0.893-0.959,p<0.001)。将 PI 临界值定为 0.30 以优化灵敏度,1081 名婴儿中有 936 名(86.5%)被正确分类。结论 在一个未见过的大型数据集中,一种开放源码算法可以识别出产后即刻面临 HIE 风险的婴儿。这有助于进行重点临床检查、转院(如有必要)和及时干预。数据可能来自第三方,且未公开。
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来源期刊
CiteScore
9.00
自引率
4.50%
发文量
90
审稿时长
6-12 weeks
期刊介绍: Archives of Disease in Childhood is an international peer review journal that aims to keep paediatricians and others up to date with advances in the diagnosis and treatment of childhood diseases as well as advocacy issues such as child protection. It focuses on all aspects of child health and disease from the perinatal period (in the Fetal and Neonatal edition) through to adolescence. ADC includes original research reports, commentaries, reviews of clinical and policy issues, and evidence reports. Areas covered include: community child health, public health, epidemiology, acute paediatrics, advocacy, and ethics.
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